| Overall Statistics |
|
Total Trades 7 Average Win 0.01% Average Loss -0.38% Compounding Annual Return 1192.565% Drawdown 1.700% Expectancy -0.322 Net Profit 3.326% Sharpe Ratio 6.634 Loss Rate 33% Win Rate 67% Profit-Loss Ratio 0.02 Alpha 0 Beta 152.168 Annual Standard Deviation 0.253 Annual Variance 0.064 Information Ratio 6.592 Tracking Error 0.253 Treynor Ratio 0.011 Total Fees $67.34 |
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Orders import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Alphas import *
from QuantConnect.Algorithm.Framework.Execution import *
from QuantConnect.Algorithm.Framework.Risk import *
from QuantConnect.Algorithm.Framework.Selection import *
from Alphas.RsiAlphaModel import RsiAlphaModel
from Alphas.EmaCrossAlphaModel import EmaCrossAlphaModel
from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
from datetime import timedelta
import numpy as np
### <summary>
### Show cases how to use the CompositeAlphaModel to define.
### </summary>
class CompositeAlphaModelFrameworkAlgorithm(QCAlgorithmFramework):
'''Show cases how to use the CompositeAlphaModel to define.'''
def Initialize(self):
self.SetStartDate(2013,10,7) #Set Start Date
self.SetEndDate(2013,10,11) #Set End Date
self.SetCash(100000) #Set Strategy Cash
# even though we're using a framework algorithm, we can still add our securities
# using the AddEquity/Forex/Crypto/ect methods and then pass them into a manual
# universe selection model using Securities.Keys
self.AddEquity("SPY")
self.AddEquity("IBM")
self.AddEquity("BAC")
self.AddEquity("AIG")
# define a manual universe of all the securities we manually registered
self.SetUniverseSelection(ManualUniverseSelectionModel())
# define alpha model as a composite of the rsi and ema cross models
self.SetAlpha(CompositeAlphaModel(RsiAlphaModel(), EmaCrossAlphaModel()))
# default models for the rest
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
self.SetExecution(ImmediateExecutionModel())
self.SetRiskManagement(NullRiskManagementModel())